Detection of Small Moving Targets in Cluttered Infrared Imagery.pdf

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Detection of Small Moving Targets in
Cluttered Infrared Imagery
Abstract
Deep convolutional neural networks can achieve remarkable results for
detecting and recognizing large
color images. However, the ability to detect small objects has yet to achieve
the same level of performance. Our
accurate detection and localization of small moving objects that are distant
from the sensor. State-of-
(such YOLOv3 and mask region
small moving objects in infrared imagery, nor do they handle temporal
information in video sequences. To overcome the limitations of these
methods, we propose the moving target indicator network (MTINet), a new
model specifically designed fo
Several versions of the MTINet are presented with different refinements (such
as spatial pyramid blocks and improved attention mechanism) to increase the
probability of detection and reduce false alarm rates. Th
Detection of Small Moving Targets in
Cluttered Infrared Imagery
Deep convolutional neural networks can achieve remarkable results for
detecting and recognizing large- and medium-sized objects in visible band
color images. However, the ability to detect small objects has yet to achieve
the same level of performance. Our focus is on applications that require the
accurate detection and localization of small moving objects that are distant
-the-art object detection and classification networks
(such YOLOv3 and mask region-based CNN) are not well suit
small moving objects in infrared imagery, nor do they handle temporal
information in video sequences. To overcome the limitations of these
methods, we propose the moving target indicator network (MTINet), a new
model specifically designed for detecting small moving targets in clutter.
Several versions of the MTINet are presented with different refinements (such
as spatial pyramid blocks and improved attention mechanism) to increase the
probability of detection and reduce false alarm rates. The MTINet is trained to
Detection of Small Moving Targets in
Deep convolutional neural networks can achieve remarkable results for
sized objects in visible band
color images. However, the ability to detect small objects has yet to achieve
focus is on applications that require the
accurate detection and localization of small moving objects that are distant
art object detection and classification networks
based CNN) are not well suited for finding
small moving objects in infrared imagery, nor do they handle temporal
information in video sequences. To overcome the limitations of these
methods, we propose the moving target indicator network (MTINet), a new
r detecting small moving targets in clutter.
Several versions of the MTINet are presented with different refinements (such
as spatial pyramid blocks and improved attention mechanism) to increase the
e MTINet is trained to

maximize the target to clutter ratio (TCR) metric, which represents the first use
of the TCR loss function for detecting moving objects. To further challenge the
MTINet, we also evaluated its performance with simulated sensor movement
mimicking effects of image stabilization processes. Finally, we also propose a
modification of the Reed-Xiaoli detector (originally developed for anomaly
detection in hyperspectral data) to enable it to detect temporal anomalies
caused by moving objects and refer to it as the temporal anomaly Reed-
Xiaolis (t-ARX) algorithm. We find that the t-ARX algorithm can achieve better
probability of detection at lower false alarm rates, while the MTINet is superior
at finding difficult targets at higher false alarm rates. We then show that the
combination of the two algorithms achieves the best overall performance.

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Detection of Small Moving Targets in Cluttered Infrared Imagery.pdf

1. Detection of Small Moving Targets in Cluttered Infrared Imagery Abstract Deep convolutional neural networks can achieve remarkable results for detecting and recognizing large color images. However, the ability to detect small objects has yet to achieve the same level of performance. Our accurate detection and localization of small moving objects that are distant from the sensor. State-of- (such YOLOv3 and mask region small moving objects in infrared imagery, nor do they handle temporal information in video sequences. To overcome the limitations of these methods, we propose the moving target indicator network (MTINet), a new model specifically designed fo Several versions of the MTINet are presented with different refinements (such as spatial pyramid blocks and improved attention mechanism) to increase the probability of detection and reduce false alarm rates. Th Detection of Small Moving Targets in Cluttered Infrared Imagery Deep convolutional neural networks can achieve remarkable results for detecting and recognizing large- and medium-sized objects in visible band color images. However, the ability to detect small objects has yet to achieve the same level of performance. Our focus is on applications that require the accurate detection and localization of small moving objects that are distant -the-art object detection and classification networks (such YOLOv3 and mask region-based CNN) are not well suit small moving objects in infrared imagery, nor do they handle temporal information in video sequences. To overcome the limitations of these methods, we propose the moving target indicator network (MTINet), a new model specifically designed for detecting small moving targets in clutter. Several versions of the MTINet are presented with different refinements (such as spatial pyramid blocks and improved attention mechanism) to increase the probability of detection and reduce false alarm rates. The MTINet is trained to Detection of Small Moving Targets in Deep convolutional neural networks can achieve remarkable results for sized objects in visible band color images. However, the ability to detect small objects has yet to achieve focus is on applications that require the accurate detection and localization of small moving objects that are distant art object detection and classification networks based CNN) are not well suited for finding small moving objects in infrared imagery, nor do they handle temporal information in video sequences. To overcome the limitations of these methods, we propose the moving target indicator network (MTINet), a new r detecting small moving targets in clutter. Several versions of the MTINet are presented with different refinements (such as spatial pyramid blocks and improved attention mechanism) to increase the e MTINet is trained to

2. maximize the target to clutter ratio (TCR) metric, which represents the first use of the TCR loss function for detecting moving objects. To further challenge the MTINet, we also evaluated its performance with simulated sensor movement mimicking effects of image stabilization processes. Finally, we also propose a modification of the Reed-Xiaoli detector (originally developed for anomaly detection in hyperspectral data) to enable it to detect temporal anomalies caused by moving objects and refer to it as the temporal anomaly Reed- Xiaolis (t-ARX) algorithm. We find that the t-ARX algorithm can achieve better probability of detection at lower false alarm rates, while the MTINet is superior at finding difficult targets at higher false alarm rates. We then show that the combination of the two algorithms achieves the best overall performance.

Detection of Small Moving Targets in Cluttered Infrared Imagery.pdf

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